Conventionally, there has been proposed a technique for considering an energy balance of the whole of a community constituted by many consumers, each of which owns a distribution power supply device (power generation apparatus) and an energy storage device (electric storage apparatus) that are connected with an electric power grid, and controlling those devices (e.g., see JP 2005-102364 A (hereinafter, referred to as “Document 1”)). Document 1 discloses a technique of dividing the community into a plurality of consumer groups and generating an operation plan for each consumer group, in order to generate an optimum operation plan with a small calculation amount even when the number of distribution power supply devices is increased.
Also there has been proposed a technique for controlling the amount of power to be stored into and to be discharged from a storage means (electric storage apparatus) based on the amount of power generated by a solar power generation means (power generation apparatus) and power consumption (e.g., see JP 2012-27214 A (hereinafter, referred to as “Document 2”)). Document 2 discloses that in order to determine an optimum charging/discharging schedule for a storage battery, a prediction period is defined, and a charging/discharging schedule for the storage battery in the prediction period is obtained by formulation in a mixed integer programming problem.
Since the technique disclosed in Document 1 divides the community into the plurality of consumer groups, the calculation amount can be more reduced, compared with a case of performing calculation for the whole of the community. However, in Document 1, the energy balance of an individual consumer is not considered, and it is therefore impossible to optimize the energy balance of an individual consumer with the technique disclosed in Document 1.
On the other hand, with the technique disclosed in Document 2, it is possible to optimize the balance of the electric power for a consumer. However, if the charging/discharging schedule is to be determined on an individual consumer's side, using an embedded apparatus in which a microcontroller is merely provided, the processing capacity insufficiency will occur. For this reason, an expensive computer is needed. On the other hand, it can be considered to provide a computer for determining the charging/discharging schedules for the plurality of consumers. In other words, it can be considered that the computer centralizedly performs the work for generating the charging/discharging schedule for an individual consumer. When adopting this configuration, an expensive computer is not needed on an individual consumer's side. However, when the number of the consumers is increased, the calculation amount for determining the charging/discharging schedules becomes huge, and accordingly a processing load on the computer also becomes high.